Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis
This study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtai...
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doaj-b8d18d269fa34431938fc86968d66b222021-07-02T19:09:47ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482018-06-0192doi:10.36001/ijphm.2018.v9i2.2703Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative AnalysisChinedu I. Ossai0School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes, GPO Box 2471 Adelaide SA 5001, AustraliaThis study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtained with lithium-ion batteries data from NASA Ames Centre repository, confirms that the SOH of the batteries is influenced by the uncertainties. This is because the random effects models show a better correlation with the experimental data than the fixed effects models that have not considered uncertainty. It is important therefore that battery prognosis is done in consideration of these parametric uncertainties, to forestall poor estimation of the SOH of the lithium-ion batteries at various stages of the lifecycle. Seeing that the presence of uncertainties could result in unwarranted failures of assets powered by the batteries, due to over-estimation of the remaining useful life (RUL) or capital loss, due to early decommissioning of efficient batteries when the RUL is under-estimated.https://papers.phmsociety.org/index.php/ijphm/article/view/2703charge capacity decaydegradation modelnonlinear mixed effect modelslithium-ion batteryreliabilityuncertainty |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chinedu I. Ossai |
spellingShingle |
Chinedu I. Ossai Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis International Journal of Prognostics and Health Management charge capacity decay degradation model nonlinear mixed effect models lithium-ion battery reliability uncertainty |
author_facet |
Chinedu I. Ossai |
author_sort |
Chinedu I. Ossai |
title |
Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis |
title_short |
Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis |
title_full |
Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis |
title_fullStr |
Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis |
title_full_unstemmed |
Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis |
title_sort |
prognostics health estimation of lithium-ion batteries in charge-decay estimation uncertainties – a comparative analysis |
publisher |
The Prognostics and Health Management Society |
series |
International Journal of Prognostics and Health Management |
issn |
2153-2648 2153-2648 |
publishDate |
2018-06-01 |
description |
This study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtained with lithium-ion batteries data from NASA Ames Centre repository, confirms that the SOH of the batteries is influenced by the uncertainties. This is because the random effects models show a better correlation with the experimental data than the fixed effects models that have not considered uncertainty. It is important therefore that battery prognosis is done in consideration of these parametric uncertainties, to forestall poor estimation of the SOH of the lithium-ion batteries at various stages of the lifecycle. Seeing that the presence of uncertainties could result in unwarranted failures of assets powered by the batteries, due to over-estimation of the remaining useful life (RUL) or capital loss, due to early decommissioning of efficient batteries when the RUL is under-estimated. |
topic |
charge capacity decay degradation model nonlinear mixed effect models lithium-ion battery reliability uncertainty |
url |
https://papers.phmsociety.org/index.php/ijphm/article/view/2703 |
work_keys_str_mv |
AT chineduiossai prognosticshealthestimationoflithiumionbatteriesinchargedecayestimationuncertaintiesacomparativeanalysis |
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1721323976794308608 |